Open AccessA two-stage genetic artificial bee colony algorithm for solving integrated operating room planning and scheduling problem with capacity constraints of downstream wards
Author(s) -
Aisha Tayyab,
Saif Ullah
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3229709
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Operating room planning and scheduling significantly affect all hospital areas, including the intensive care unit and downstream wards. Planning and scheduling operating rooms integrated with intensive care units and downstream wards can lead to more stable plans and schedules that are less prone to cancellations. Thus, this study considers the operating room’s capacity and downstream units while making surgery-related decisions. A mixed integer linear programming model for integrated planning consisting of two stages is proposed. The first stage model maximizes the scheduled surgical time of all operating rooms. In contrast, the second stage model aims to minimize the makespan of patients in operating rooms by incorporating sequence-dependent setup time and the capacity constraints of all resources under consideration at both stages. A two-stage genetic artificial bee colony algorithm (TGABC) hybrid of genetic algorithm and artificial bee colony algorithm is proposed to solve the model. Taguchi design of experiments is employed to fine-tune the parameters of the proposed TGABC algorithm. Experiments are designed to evaluate the performance of the proposed TGABC algorithm with generated instances mimicking real data for different-sized problems, and results are presented. The proposed method is compared with the exact method and three standard metaheuristics. It provides near-optimal results in comparatively shorter CPU time. Moreover, it outperforms the genetic algorithm, artificial bee colony algorithm, and simulated annealing in terms of solution quality compared on the considered test instances.